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schedule.Rmd
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schedule.Rmd
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---
title: "Schedule"
---
| **Week** | **Date** | **Lead Instructor** | **Topic** | **Functions introduced** | **Assigned** | **Due** | **Reading** |
| :------- | :----------: | :----------------------------: | :----------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: | :----------: | :-----------: | :---------: |
| 1a | 9/28/20 | - | No Class (Yom Kippur) | | | | |
| 1b | 9/30/20 | Joe | [Intro to course](slides/w1p2-intro/w1p2.pdf) | | Final<br/>Vote! | | [HML Ch. 1](https://bradleyboehmke.github.io/HOML/intro.html) |
| 2a | 10/5/20 | Daniel | [Inference vs. Prediction<br/>Bias-Variance Tradeoff<br/>Regression vs Classification](slides/w2p1-inf-v-pred.html) | | <br/>Vote! | | APM: Ch. 1, ISLR: Ch. 2.1 |
| 2b | 10/7/20 | Guest Lecture: <br/>[Sondra Stegenga](https://twitter.com/stegso) | [Ethics in Machine Learning](slides/DataEthics_Fall2020.pdf) | | <br/>Vote! | | [Floridi & Taddeo (2016)](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360) |
| 3a | 10/12/20 | Joe | [Train and Test Splits<br/> *k*-fold CV](slides/w3p1-resampling/w3p1.pdf) | `initial_split`<br/>`train()`<br/>`test()`<br/>`vfold_cv()` | <br/>Vote! | | ISLR: Ch. 2.2 |
| 3b | 10/14/20 | Joe | [Lab 1: Resampling]() | | [Lab 1]()<br/>Vote! | Data Quiz | ISLR: Ch. 5.1, APM: Ch. 5, APM: Ch. 11 |
| 4a | 10/19/20 | Joe | [Extending `lm`: Ridge, Lasso, Elastic net](slides/w4p1-penalized-regression/w4p1.pdf) | Choose “model function”, `set_engine()`, `set_mode()`, `fit_resamples()` | <br/>Vote! | | ISLR: Ch. 6.1, [HML: Ch. 6](https://bradleyboehmke.github.io/HOML/regularized-regression.html), APM: Ch. 6.4 |
| 4b | 10/21/20 | Joe | [Lab 2: Penalized Regression]() | `metric_set()`, `collect_metrics()`, `select_best()`, `tune_grid()`, `grid_regular()`, `show_best()` | [Lab 2]()<br/>Vote! | Lab 1 | |
| 5a | 10/26/20 | Daniel | [Feature engineering](slides/w5p1-feature-engineering.html) | [{recipes}](https://tidymodels.github.io/recipes/) | <br/>Vote! | | [FE: Ch. 1](http://www.feat.engineering/intro-intro.html), [HML: Ch. 3](https://bradleyboehmke.github.io/HOML/engineering.html) |
| 5b | 10/28/20 | Daniel | [Lab 3: Feature Engineering](https://www.kaggle.com/djanderson07/lab-3) | | [Lab 3]()<br/>Vote! | Lab 2 | |
| 6a | 11/2/20 | Joe | [*K*-nearest neighbor](slides/w6p1-knn/w6p1.pdf) | `nearest_neighbor()`, `grid_max_entropy()`, `autoplot()` | <br/>Vote! | Prelim fit 1 | [HML: Ch. 8](https://bradleyboehmke.github.io/HOML/knn.html) |
| 6b | 11/4/20 | Daniel | [Lab 4: Guided walkthrough with HPC & *K*NN](slides/w6p2-hpc.html) | | [Lab 4]() | Lab 3 | |
| 7a | 11/9/20 | Daniel | [Decision trees](slides/w7p1-decision-trees.html) | | | | [HML: Ch. 9](https://bradleyboehmke.github.io/HOML/DT.html) |
| 7b | 11/11/20 | Daniel | [Bagged trees](slides/w7p2-bagging.html) | | | Lab 4 | [HML: Ch. 10](https://bradleyboehmke.github.io/HOML/bagging.html) |
| 8a | 11/16/20 | Joe | [Random forests](slides/w8p1-random-forest/w8p1.pdf) | `{workflows}`,`extract()` | | | [HML: Ch. 11 ](https://bradleyboehmke.github.io/HOML/random-forest.html) |
| 8b | 11/18/20 | Joe | [Lab 5: Tree-based models]() & [Quick review](slides/w8p2-review.html) | | [Lab 5]() | Prelim fit 2 | |
| 9a | 11/23/20 | Daniel | [Boosted Trees 1](slides/w9p1-boosted-trees-1.html) | | | | [HML Ch. 12](https://bradleyboehmke.github.io/HOML/gbm.html) |
| 9b | 11/25/20 | Daniel | [Boosted Trees 2](slides/w9p2-boosted-trees-2.html) | | | Lab 5 | |
| 10a | 11/30/20 | - | Work Day | | | | [HML Ch. 13](https://bradleyboehmke.github.io/HOML/deep-learning.html), [HML Ch. 15](https://bradleyboehmke.github.io/HOML/stacking.html) |
| 10b | 12/2/20 | Daniel | [Intro to neural nets w/Keras & Tensorflow](slides/w10p2-neural-nets.html) | | | | |
| Finals Week | 12/7/20 | | | | | Final Project | (by 11:59 PM) |